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Judgment and Decision Making by Individuals and Teams: Issues, Models, and Applications
Published in Don Harris, Wen-Chin Li, Decision Making in Aviation, 2017
Kathleen L. Mosier, Ute M. Fischer
Mental models and schemata. The term mental model is widely used in explanations of how experts make decisions in complex operational environments. Experts are said to have mental models of the domain they are operating in, their task, their current situation, and, if decision making takes place in a team context, of their team (Cannon-Bowers, Salas, & Converse, 1993; Orasanu, 1994; Rouse, Cannon-Bowers, & Salas, 1992). These models are believed to direct decision makers’ attention to relevant cues and information, to guide their problem understanding and action selection, and to support coordinated action with others. Although the term mental model undeniably has explanatory appeal, its meaning remains rather elusive. What are mental models? How do they differ from traditional cognitive psychology concepts, such as schemata or scripts?
Individual and Team Decision Making Under Stress: Theoretical Underpinnings
Published in Florian Jentsch, Michael Curtis, Eduardo Salas, Simulation in Aviation Training, 2017
Janis A. Cannon-Bowers, Eduardo Salas
In the area of cognitive psychology, researchers have suggested that mental models are important more generally to the understanding of how humans interact and cope with the world (Rouse & Morris, 1986). For example, Williams, Hollan, and Stevens (1983) maintain that mental models allow people to predict and explain system behavior, and help them to understand the relationship between system components and events. Wickens (1984) contended further that mental models provide a source of people's expectations. In an even more general view, Johnson-Laird (1983) suggested that people "understand the world by constructing working models of it in their mind" (p. 10). Mental models enable people to draw inferences and make predictions, to understand phenomena, to decide what actions to take, to control system execution, and to experience events vicariously (Johnson-Laird, 1983).
Dynamic Modeling of New Marketplaces: Techniques for Dealing with Uncertainty
Published in Mark Paich, Corey Peck, Jason Valant, Pharmaceutical Product Branding Strategies, 2009
Mark Paich, Corey Peck, Jason Valant
As was discussed in chapters 1 and 2, Dynamic Modeling is a useful way to make mental models explicit and testable in a concise and rigorous manner. Our cumulative experience over many years of building dynamic models is that augmenting mental models with simulation improves the quality of analysis and results in better strategic decisions (3). Mental models provide creative insights that no computerized technique can match, and combining them with simulation can provide increased clarity and consistency in the decision-making process. In successful Dynamic Modeling projects, the process of building a simulation model synergistically improves the quality of the mental model that, in turn, produces an improved simulation model from which better strategic decisions can be made.
Explainable and secure artificial intelligence: taxonomy, cases of study, learned lessons, challenges and future directions
Published in Enterprise Information Systems, 2023
Khalid A. Eldrandaly, Mohamed Abdel-Basset, Mahmoud Ibrahim, Nabil M. Abdel-Aziz
The word mental model refers to a notion drawn from cognitive psychology that describes how individuals think about how things operate. The mental model may also be thought of as an internal representation of external elements, and it is crucial in cognition, decision making, and reasoning. A mental model is a depiction of how users comprehend the system (Sina, Zarei, and Ragan 2021). Internal conceptualisations, which include users’ views and knowledge of system behaviour, will influence their engagement with the systems (Xie, Anthony Chen, and Gao 2019). The effective use of intelligent systems is dependent on user mental models, which must be solicited and assessed. They can be elicited in the XAI Program utilising certain types of structured interview in which users express their knowledge of the AI system, with the protocols compared for their propositional congruence with expert explanations (Hoffman, Klein, and Mueller 2018). Mental models can be constructed to understand and predict the robot’s behaviour, and behaviour of autonomous intelligent systems (Rueben et al. 2020). (Perlmutter, Kernfeld, and Cakmak 2016) show how representations affect the correctness of people’s mental models of what robots can perceive, and the effectiveness and efficiency for communicating mission orders.
Integrating Transparency, Trust, and Acceptance: The Intelligent Systems Technology Acceptance Model (ISTAM)
Published in International Journal of Human–Computer Interaction, 2022
From Vorm and Miller’s perspective (and the perspective of other researchers, see above), providing information with regard to system parameters and logic, (i.e., how a system works, including its policies, logic, and limitations) can help users build appropriate mental models of systems and help users navigate or explain unexpected events. A mental model is a person’s mental representation of what something is, what it is for, and how it works (Rouse & Morris, 1986). Users build mental models of systems through their experiences and interactions with them, which in turn determines subsequent interactions. Systems that restrict or hide information, therefore, can dramatically skew users' understanding of those systems (Marwick & Boyd, 2011; Viégas et al., 2006), which in turn influences how users use and interact with those systems. Mental models need to be accurate and appropriate in order to help users interact with a system and understand how to use it. In addition, mental models need to help users understand the reasoning of what lies beneath computations and processes that make the system function. As such, Vorm and Miller’s explanation vector of system parameters and logic suggests that good transparency design should permit users to have strong and clear access to underlying system parameters and logic to help them build such models. Their perspective on the need for transparency with regard to system parameters and logic maps nearly identically onto Bernstein’s transparency for system process concept.
Introducing ‘Concept Question’ writing assignments into upper-level engineering courses
Published in European Journal of Engineering Education, 2021
Kirsten A. Davis, William A. Mogg, David P. Callaghan, Greg R. Birkett, David B. Knight, Katherine R. O’Brien
Conceptual understanding refers to an individual’s beliefs about how concepts relate to each other, forming mental models. Mental models are the collections of concepts and beliefs that the individual uses to explain phenomena (Streveler et al. 2014). Engineering graduates have been found to lack understanding of foundational engineering concepts, making conceptual change a focus in engineering education research (Streveler et al. 2014). Conceptual knowledge is differentiated from factual knowledge (i.e., knowledge of terminology), procedural knowledge (i.e., knowledge of techniques and methods), and metacognitive knowledge (Krathwohl 2002). In engineering education, it can be easy for students to focus on procedural knowledge (i.e., how to solve a problem) without developing the associated conceptual knowledge (Venters, McNair, and Paretti 2014). Such an approach hinders students from developing expertise in the field, which requires both procedural and conceptual knowledge (Bransford, Brown, and Cocking 2000; Venters, McNair, and Paretti 2014). To help students develop conceptual understanding, it is important to support students’ ability to develop mental models and general problem-solving approaches within a domain (Greeno, Collins, and Resnick 1996). Our study presents an example of how such support can be provided within upper-level engineering courses, where it is important for students to move beyond abstract and compartmentalised understanding of engineering concepts toward learning to apply these concepts in more complex and contextualised situations (Bornasal et al. 2018; Lord and Chen 2014).